Operations | Monitoring | ITSM | DevOps | Cloud

-- Key Takeaways: The Harness MCP server is an MCP-compatible interface that lets AI agents discover, query, and act on Harness resources across CI/CD, GitOps, Feature Flags, Cloud Cost Management, Security Testing, Resilience Testing, Internal Developer Portal, and more. -- The first wave of MCP servers followed a natural pattern: take every API endpoint, wrap it in a tool definition, and expose it to the LLM.

E2E Testing Has a New Bottleneck, and It's Not the Code End-to-end (E2E) testing has always been the hardest part of a QA strategy. You're simulating real users, navigating real flows, validating real outcomes across browsers, environments, and data states that never hold still. Traditional test automation tackled this with scripts: rigid, deterministic sequences tied to element selectors and hard-coded values. They worked until the UI changed. Or the data changed.

Outages in distributed systems are inevitable, making resilience testing essential in the SDLC. It must be continuous, covering failures, load, and disasters. Delayed validation creates “resilience debt,” increasing risk. A holistic approach—combining chaos, load, and DR testing—plus cross-team collaboration and AI-driven insights improves reliability and reduces impact. Modern software delivery has dramatically accelerated.

--- ‍Key Takeaways --- AI can generate code in seconds. It still can’t ship software safely. That gap isn’t about model quality or prompt engineering. It’s about context, and most software organizations don’t have a system that accurately reflects how pipelines, services, environments, policies, and teams actually relate to each other. Without that context, AI doesn’t automate delivery. It amplifies risk.

AI is changing both what you build and how you build it - at the same time. Today, Harness is announcing two new products to secure both: AI Security, a new product to discover, test, and protect AI running in your applications, and Secure AI Coding, a new capability of Harness SAST that secures the code your AI tools are writing.

This is part 1 of a five-part series on building production-grade AI engineering systems. Across this series, we will cover: Most teams experimenting with AI coding agents focus on prompts. That is the wrong starting point. Before you optimize how an agent thinks, you must standardize what it sees. AI agents do not primarily fail because of reasoning limits. They fail because of environmental ambiguity.

APIs have revolutionized how web and web app developers interact with data, whether for personal use or business. One of our most profound responsibilities as API developers is to protect our endpoints from being hacked. Even with essential safeguards in place, our websites can be vulnerable. This post discusses seven causes of API failures and how to fix them.

Speed is exposing the cracks. Our research shows that 69% of heavy AI users now face frequent deployment issues. To capture the ROI of AI, leaders must shift focus from code generation to delivery modernization. standardizing foundations and automating the "manual middle" that leads to developer burnout. Over the last few years, something fundamental has changed in software development.